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  <h1>Source code for torch_tensorrt.fx.input_tensor_spec</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">NamedTuple</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span>

<span class="kn">import</span> <span class="nn">torch</span>

<span class="kn">from</span> <span class="nn">.types</span> <span class="kn">import</span> <span class="n">Shape</span><span class="p">,</span> <span class="n">ShapeRange</span>
<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">get_dynamic_dims</span>


<span class="k">def</span> <span class="nf">generate_input_specs</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">lower_setting</span><span class="p">,</span> <span class="n">additional_inputs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="c1"># dynamic_batch is TRT only flag.</span>
    <span class="k">if</span> <span class="p">(</span>
        <span class="ow">not</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">explicit_batch_dimension</span>
        <span class="ow">or</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">dynamic_batch</span> <span class="ow">is</span> <span class="kc">False</span>
    <span class="p">):</span>
        <span class="k">return</span> <span class="n">InputTensorSpec</span><span class="o">.</span><span class="n">from_tensors</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>

    <span class="c1"># If we don&#39;t have additional inputs, we assume the first dimension</span>
    <span class="c1"># is the dynamic batch dimension. Otherwise, we use the additional</span>
    <span class="c1"># inputs to determine the batch dimension.</span>
    <span class="k">if</span> <span class="n">additional_inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">InputTensorSpec</span><span class="o">.</span><span class="n">from_tensors_with_dynamic_batch_size</span><span class="p">(</span>
            <span class="n">inputs</span><span class="p">,</span>
            <span class="p">(</span>
                <span class="mi">0</span><span class="p">,</span>
                <span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
                <span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
            <span class="p">),</span>
            <span class="n">lower_setting</span><span class="o">.</span><span class="n">opt_profile_replica</span><span class="p">,</span>
        <span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">batch_dims</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">additional_inputs</span><span class="p">):</span>
            <span class="n">found_batch_dim</span> <span class="o">=</span> <span class="kc">False</span>

            <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">values</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">j</span><span class="o">.</span><span class="n">shape</span><span class="p">)):</span>
                <span class="k">if</span> <span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">values</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
                    <span class="k">assert</span> <span class="p">(</span>
                        <span class="n">found_batch_dim</span> <span class="ow">is</span> <span class="kc">False</span>
                    <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;We&#39;ve already found a batch dim, </span><span class="si">{</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">j</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">.&quot;</span>
                    <span class="n">batch_dims</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
                    <span class="n">found_batch_dim</span> <span class="o">=</span> <span class="kc">True</span>

            <span class="k">if</span> <span class="ow">not</span> <span class="n">found_batch_dim</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                    <span class="sa">f</span><span class="s2">&quot;Failed to find batch dimension because shapes are the same, </span><span class="si">{</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">&quot;</span>
                <span class="p">)</span>

        <span class="k">return</span> <span class="n">InputTensorSpec</span><span class="o">.</span><span class="n">from_tensors_with_dynamic_batch_size</span><span class="p">(</span>
            <span class="n">inputs</span><span class="p">,</span>
            <span class="p">(</span>
                <span class="mi">0</span><span class="p">,</span>
                <span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
                <span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
            <span class="p">),</span>
            <span class="n">lower_setting</span><span class="o">.</span><span class="n">opt_profile_replica</span><span class="p">,</span>
            <span class="n">batch_dims</span><span class="p">,</span>
        <span class="p">)</span>


<div class="viewcode-block" id="InputTensorSpec"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.InputTensorSpec">[docs]</a><span class="k">class</span> <span class="nc">InputTensorSpec</span><span class="p">(</span><span class="n">NamedTuple</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    This class contains the information of a input tensor.</span>

<span class="sd">    shape: shape of the tensor.</span>

<span class="sd">    dtype: dtyep of the tensor.</span>

<span class="sd">    device: device of the tensor. This is only used to generate inputs to the given model</span>
<span class="sd">        in order to run shape prop. For TensorRT engine, inputs have to be on cuda device.</span>

<span class="sd">    shape_ranges: If dynamic shape is needed (shape has dimensions of -1), then this field</span>
<span class="sd">        has to be provided (default is empty list). Every shape_range is a tuple of three</span>
<span class="sd">        tuples ((min_input_shape), (optimized_input_shape), (max_input_shape)). Each shape_range</span>
<span class="sd">        is used to populate a TensorRT optimization profile.</span>
<span class="sd">        e.g. If the input shape varies from (1, 224) to (100, 224) and we want to optimize</span>
<span class="sd">        for (25, 224) because it&#39;s the most common input shape, then we set shape_ranges to</span>
<span class="sd">        ((1, 224), (25, 225), (100, 224)).</span>

<span class="sd">    has_batch_dim: Whether the shape includes batch dimension. Batch dimension has to be provided</span>
<span class="sd">        if the engine want to run with dynamic shape.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">shape</span><span class="p">:</span> <span class="n">Shape</span>
    <span class="n">dtype</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span>
    <span class="n">device</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
    <span class="n">shape_ranges</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">ShapeRange</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">has_batch_dim</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>

    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">from_tensor</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">tensor</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;InputTensorSpec&quot;</span><span class="p">:</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Produce an InputTenosrSpec named tuple which contains the</span>
<span class="sd">        information of the given PyTorch tensor.</span>

<span class="sd">        Args:</span>
<span class="sd">            tensor (torch.Tensor): A PyTorch tensor.</span>

<span class="sd">        Returns:</span>
<span class="sd">            An InputTensorSpec named tuple.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>

    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">from_tensors</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">tensors</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="s2">&quot;InputTensorSpec&quot;</span><span class="p">]:</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Produce a list of InputTenosrSpec named tuples which contain</span>
<span class="sd">        the information of all the given PyTorch tensors.</span>

<span class="sd">        Args:</span>
<span class="sd">            tensors (Iterable[torch.Tensor]): A list of PyTorch tensors.</span>

<span class="sd">        Returns:</span>
<span class="sd">            A list of InputTensorSpec named tuples.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">))</span>
        <span class="k">return</span> <span class="p">[</span><span class="bp">cls</span><span class="o">.</span><span class="n">from_tensor</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">]</span>

    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">from_tensors_with_dynamic_batch_size</span><span class="p">(</span>
        <span class="bp">cls</span><span class="p">,</span>
        <span class="n">tensors</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
        <span class="n">batch_size_range</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
        <span class="n">opt_profile_replica</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
        <span class="n">batch_dims</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="s2">&quot;InputTensorSpec&quot;</span><span class="p">]:</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Produce a list of InputTenosrSpec named tuples which would contain</span>
<span class="sd">        the information of all the given PyTorch tensors. The produced input</span>
<span class="sd">        tensor specs will treat all tensors&#39; first dimension as batch dimension</span>
<span class="sd">        and mark them as dynmaic.</span>

<span class="sd">        Args:</span>
<span class="sd">            tensors (Sequence[torch.Tensor]): A list of PyTorch tensors.</span>
<span class="sd">            batch_size_range (Tuple[int, int, int]): The first integer indicates</span>
<span class="sd">                the smallest batch size allowed. The second integer indiceates</span>
<span class="sd">                the batch size that we&#39;ll optimize for. The third integer indicates</span>
<span class="sd">                the largest batch size allowed.</span>
<span class="sd">            opt_profile_replica (int): If dynamic shape is enabled, each execution</span>
<span class="sd">                context requires a different optimization profile. This arg determines</span>
<span class="sd">                how many optimization profile replicas we want to produce.</span>
<span class="sd">            batch_dims (Optional[List[int]]): The batch dim might not be the leading dim</span>
<span class="sd">                and allow user to specify the batch dims using this arg. Default we treat</span>
<span class="sd">                dim 0 as the batch dim.</span>

<span class="sd">        Returns:</span>
<span class="sd">            A list of InputTensorSpec named tuples with dynamic ranges.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">batch_dims</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">batch_dims</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span>

        <span class="n">input_specs</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">tensors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">batch_dims</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tensors</span><span class="p">):</span>
            <span class="n">batch_dim</span> <span class="o">=</span> <span class="n">batch_dims</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
            <span class="k">assert</span> <span class="n">batch_size</span> <span class="o">==</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span>
                <span class="n">batch_dim</span>
            <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;The </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">th tensor (shape: </span><span class="si">{</span><span class="n">tensor</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">) doesn&#39;t have the correct batch size: </span><span class="si">{</span><span class="n">batch_size</span><span class="si">}</span><span class="s2">.&quot;</span>
            <span class="n">shape</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
            <span class="n">shape</span><span class="p">[</span><span class="n">batch_dim</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
            <span class="n">shape_ranges</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">ShapeRange</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">batch_dim</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">bs</span><span class="p">]</span> <span class="o">+</span> <span class="n">shape</span><span class="p">[</span><span class="n">batch_dim</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:])</span> <span class="k">for</span> <span class="n">bs</span> <span class="ow">in</span> <span class="n">batch_size_range</span><span class="p">)]</span> <span class="o">*</span> <span class="n">opt_profile_replica</span>  <span class="c1"># type: ignore[list-item]</span>
            <span class="n">input_specs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
                <span class="bp">cls</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">),</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">shape_ranges</span><span class="p">)</span>
            <span class="p">)</span>

        <span class="k">return</span> <span class="n">input_specs</span>

    <span class="k">def</span> <span class="nf">to_random_tensor</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">get_dynamic_dims</span><span class="p">(</span><span class="n">shape</span><span class="p">)):</span>
            <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape_ranges</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
        <span class="k">elif</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_batch_dim</span><span class="p">:</span>
            <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">+</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">create_inputs_from_specs</span><span class="p">(</span><span class="n">input_specs</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="s2">&quot;InputTensorSpec&quot;</span><span class="p">]):</span>
        <span class="n">inputs</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="k">for</span> <span class="n">spec</span> <span class="ow">in</span> <span class="n">input_specs</span><span class="p">:</span>
            <span class="n">inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">spec</span><span class="o">.</span><span class="n">to_random_tensor</span><span class="p">())</span>

        <span class="k">return</span> <span class="n">inputs</span></div>
</pre></div>

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